Job Purpose
Lead deep-dive data analysis and root-cause analysis for an assigned domain (either Partners/Restaurant or Logistics/Drivers). Size the biggest cost and quality drivers, design and pilot corrective actions, and mentor a Data Analysis Specialist turning operational and service data into prioritized, measurable improvement opportunities that protect and improve the customer experience.
Job Responsibilities
- Own exploratory data analysis (EDA) and root-cause analysis (RCA) for the assigned domain analyzing operational and service events by service, attribution, reason code, geography and time.
- Quantify each root cause and score it on impact vs effort; build and maintain the prioritized RCA backlog for the domain.
- Design corrective actions with predicted impact; run pilots with control groups where possible and measure results against targets and customer-experience metrics (CSAT, retention, loyalty).
- Identify process anomalies and abuse patterns that drive avoidable cost within the domain, while protecting genuine customers and their experience.
- Contribute to improvements in automated decision and service logic thresholds and accuracy with Technology and Product, so outcomes are correct by default.
- Ensure correct data attribution and integrity; maintain domain baselines and dashboards.
- Mentor and review the work of the Data Analysis Specialist and support the Supervisor on target-setting.
- Present findings, opportunities and recommendations to the Supervisor and Team Lead, and partner with the Customer Experience team.
Requirements
- Bachelor's degree in Data Science, Statistics, Engineering, Economics, Business or a related field.
- 3+ years in data or business analytics, delivering insights that drove measurable action.
- Background in FMCG, food delivery, q-commerce or retail. Domain exposure to restaurant operations OR logistics/last-mile is a strong plus.
- Solid understanding of customer-experience and satisfaction drivers; comfortable balancing cost-efficiency with CSAT, retention and loyalty.
- Strong SQL (must-have); advanced Excel/Google Sheets; hands-on with a BI tool; comfortable with large transactional datasets.
- Structured RCA methods; basics of experiment / A-B and pilot design; Python or R for analysis is a plus.
- English required
